For a positive head CT scan, we will refer to examination-level hemorrhage detection as the task of retrieving the images that contain signs of ICH; and image-level hemorrhage detection as the task of highlighting these findings within the retrieved images. We rephrase both examination- and image-level hemorrhage detection as MIL binary classification problems [1]}, [2]}, [3]} (see sec:learningparadigms for details on supervised learning and MIL), and evaluate the performance of models trained with local (image-level) annotations and global (examination-level) labels. We refer to the former as a strong learner (\({\mathcal {SL}}\) ), as it is trained via strong supervision, and weak learner (\({\mathcal {WL}}\) ) to the latter, since it only uses weak supervision.
